Provably End-to-end Label-Noise Learning without Anchor Points

Abstract

In label-noise learning, the transition matrix plays a key role in building statistically consistent classifiers. Existing consistent estimators for the transition matrix have been developed by exploiting anchor points. However, the anchor-point assumption is not always satisfied in real scenarios. In this paper, we propose an end-to-end framework for solving label-noise learning without anchor points, in which we simultaneously optimize two objectives: the cross entropy loss between the noisy label and the predicted probability by the neural network, and the volume of the simplex formed by the columns of the transition matrix. Our proposed framework can identify the transition matrix if the clean class-posterior probabilities are sufficiently scattered. This is by far the mildest assumption under which the transition matrix is provably identifiable and the learned classifier is statistically consistent. Experimental results on benchmark datasets demonstrate the effectiveness and robustness of the proposed method.

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Cited by in corpus (6)

• Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
• Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
• Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
• Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
• A Second-Order Approach to Learning with Instance-Dependent Label Noise
• Policy Learning Using Weak Supervision